{"cells":[{"cell_type":"markdown","metadata":{"id":"AB155B13302D459CBAAABA327849B76D","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"# 说明\n特征工程部分照搬社区的baseline未作改动，仅仅进行了模型的部分参数调整和基于stacking的模型融合。\n另外，由于是输出概率，后续按照回归去做，故删除了不平衡样本的处理，一开始当成分类去做，最高只能到0.8+，按照回归轻松0.9+\n参数仅做了简单的调整，非最优，线下0.9361018703876826，线上验证0.93536922"},{"cell_type":"markdown","metadata":{"id":"C325760DBE164B398588C602C8C376BC","jupyter":{},"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 查看数据"},{"cell_type":"code","execution_count":1,"metadata":{"collapsed":false,"id":"0A028E17E34B4CFEABE9DFF956D89741","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus'] = False"},{"cell_type":"code","execution_count":2,"metadata":{"collapsed":false,"id":"001F87A7D41644E4BA2EA25513B0D1B3","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"train=pd.read_csv(r'/home/mw/input/data9803/train_set.csv')\ntest=pd.read_csv('/home/mw/input/data9803/test_set.csv')"},{"cell_type":"code","execution_count":3,"metadata":{"collapsed":false,"id":"B8C543716B964973872549E7B4DFDFAA","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"data = pd.concat([train.drop(['y'],axis=1),test],axis=0).reset_index(drop=True)"},{"cell_type":"code","execution_count":4,"metadata":{"id":"D7CE94A95A2544AE808C26A4B1901BA7","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true,"collapsed":false},"outputs":[{"output_type":"stream","text":"job :   ['management' 'technician' 'admin.' 'services' 'retired' 'student'\n 'blue-collar' 'unknown' 'entrepreneur' 'housemaid' 'self-employed'\n 'unemployed']\nmarital :   ['married' 'divorced' 'single']\neducation :   ['tertiary' 'primary' 'secondary' 'unknown']\ndefault :   ['no' 'yes']\nhousing :   ['yes' 'no']\nloan :   ['no' 'yes']\ncontact :   ['unknown' 'cellular' 'telephone']\nmonth :   ['may' 'apr' 'jul' 'jun' 'nov' 'aug' 'jan' 'feb' 'dec' 'oct' 'sep' 'mar']\npoutcome :   ['unknown' 'other' 'failure' 'success']\n","name":"stdout"}],"source":"# 对object型数据查看unique\nstr_features = []\nnum_features=[]\nfor col in train.columns:\n    if train[col].dtype=='object':\n        str_features.append(col)\n        print(col,':  ',train[col].unique())\n    if train[col].dtype=='int64' and col not in ['ID','y']:\n        num_features.append(col)"},{"cell_type":"markdown","metadata":{"id":"29CB9E135078402DBE2F479858631C1D","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 特征工程"},{"cell_type":"code","execution_count":6,"metadata":{"collapsed":true,"id":"928046C510F54165A50D2FFBEFB4A5B5","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"from scipy.stats import chi2_contingency       # 数值型特征检验，检验特征与标签的关系\nfrom scipy.stats import f_oneway,ttest_ind     # 分类型特征检验，检验特征与标签的关系"},{"cell_type":"code","execution_count":7,"metadata":{"collapsed":true,"id":"B5098300108B4CB784E1E1D8A1A7AF40","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"#----------数据集处理--------------#\nfrom sklearn.model_selection import train_test_split        # 划分训练集和验证集\nfrom sklearn.model_selection import KFold,StratifiedKFold   # k折交叉\nfrom imblearn.combine import SMOTETomek,SMOTEENN            # 综合采样\nfrom imblearn.over_sampling import SMOTE                    # 过采样\nfrom imblearn.under_sampling import RandomUnderSampler      # 欠采样\n\n#----------数据处理--------------#\nfrom sklearn.preprocessing import StandardScaler # 标准化\nfrom sklearn.preprocessing import OneHotEncoder  # 热独编码\nfrom sklearn.preprocessing import OrdinalEncoder"},{"cell_type":"markdown","metadata":{"id":"AB4FDF679736489B86708802E2B4E05B","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"## 特征处理"},{"cell_type":"markdown","metadata":{"id":"B76FEED533744B93AD63ED6825716E5C","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"**连续变量即数值化数据做标准化处理**"},{"cell_type":"code","execution_count":8,"metadata":{"collapsed":true,"id":"DD0BE0C3EF0D4755A0F255565CA064B3","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"# 异常值处理\ndef outlier_processing(dfx):\n    df = dfx.copy()\n    q1 = df.quantile(q=0.25)\n    q3 = df.quantile(q=0.75)\n    iqr = q3 - q1\n    Umin = q1 - 1.5*iqr\n    Umax = q3 + 1.5*iqr \n    df[df>Umax] = df[df<=Umax].max()\n    df[df<Umin] = df[df>=Umin].min()\n    return df"},{"cell_type":"code","execution_count":9,"metadata":{"collapsed":true,"id":"353D496D273C4456A8EF7C663D1C81C1","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"train['age']=outlier_processing(train['age'])\ntrain['day']=outlier_processing(train['day'])\ntrain['duration']=outlier_processing(train['duration'])\ntrain['campaign']=outlier_processing(train['campaign'])\n\n\ntest['age']=outlier_processing(test['age'])\ntest['day']=outlier_processing(test['day'])\ntest['duration']=outlier_processing(test['duration'])\ntest['campaign']=outlier_processing(test['campaign'])"},{"cell_type":"code","execution_count":10,"metadata":{"id":"605C7188AA404524AA2B935E363D1E6C","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>balance</th>\n","      <th>day</th>\n","      <th>duration</th>\n","      <th>campaign</th>\n","      <th>pdays</th>\n","      <th>previous</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>25317.000000</td>\n","      <td>25317.000000</td>\n","      <td>25317.000000</td>\n","      <td>25317.000000</td>\n","      <td>25317.000000</td>\n","      <td>25317.000000</td>\n","      <td>25317.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>40.859502</td>\n","      <td>1357.555082</td>\n","      <td>15.835289</td>\n","      <td>234.235138</td>\n","      <td>2.391437</td>\n","      <td>40.248766</td>\n","      <td>0.591737</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>10.387365</td>\n","      <td>2999.822811</td>\n","      <td>8.319480</td>\n","      <td>175.395559</td>\n","      <td>1.599851</td>\n","      <td>100.213541</td>\n","      <td>2.568313</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>18.000000</td>\n","      <td>-8019.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>-1.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>33.000000</td>\n","      <td>73.000000</td>\n","      <td>8.000000</td>\n","      <td>103.000000</td>\n","      <td>1.000000</td>\n","      <td>-1.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>39.000000</td>\n","      <td>448.000000</td>\n","      <td>16.000000</td>\n","      <td>181.000000</td>\n","      <td>2.000000</td>\n","      <td>-1.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>48.000000</td>\n","      <td>1435.000000</td>\n","      <td>21.000000</td>\n","      <td>317.000000</td>\n","      <td>3.000000</td>\n","      <td>-1.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>70.000000</td>\n","      <td>102127.000000</td>\n","      <td>31.000000</td>\n","      <td>638.000000</td>\n","      <td>6.000000</td>\n","      <td>854.000000</td>\n","      <td>275.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                age        balance           day      duration      campaign  \\\n","count  25317.000000   25317.000000  25317.000000  25317.000000  25317.000000   \n","mean      40.859502    1357.555082     15.835289    234.235138      2.391437   \n","std       10.387365    2999.822811      8.319480    175.395559      1.599851   \n","min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n","25%       33.000000      73.000000      8.000000    103.000000      1.000000   \n","50%       39.000000     448.000000     16.000000    181.000000      2.000000   \n","75%       48.000000    1435.000000     21.000000    317.000000      3.000000   \n","max       70.000000  102127.000000     31.000000    638.000000      6.000000   \n","\n","              pdays      previous  \n","count  25317.000000  25317.000000  \n","mean      40.248766      0.591737  \n","std      100.213541      2.568313  \n","min       -1.000000      0.000000  \n","25%       -1.000000      0.000000  \n","50%       -1.000000      0.000000  \n","75%       -1.000000      0.000000  \n","max      854.000000    275.000000  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":"train[num_features].describe()"},{"cell_type":"markdown","metadata":{"id":"FA30A072639C418283A5F9FF7BB70A5A","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"**分类变量做编码处理**"},{"cell_type":"code","execution_count":11,"metadata":{"collapsed":true,"id":"B843C59639844E1A872AECAFE7926878","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"dummy_train=train.join(pd.get_dummies(train[str_features])).drop(str_features,axis=1).drop(['ID','y'],axis=1)\ndummy_test=test.join(pd.get_dummies(test[str_features])).drop(str_features,axis=1).drop(['ID'],axis=1)"},{"cell_type":"markdown","metadata":{"id":"F2EA03BA7FAA45FF85F2CEDA21AB2F25","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"## 统计检验与特征筛选 \n\n\n**连续变量-连续变量  相关分析**\n\n**连续变量-分类变量  T检验/方差分析**\n\n**分类变量-分类变量  卡方检验**"},{"cell_type":"markdown","metadata":{"id":"500456AC13CA4815858E06D09EE17A9E","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"**对类别标签（离散变量）用卡方检验分析重要性**\n\n卡方检验认为显著水平大于95%是差异性显著的，这里即看p值是否是p>0.05，若p>0.05，则说明特征不会呈现差异性"},{"cell_type":"code","execution_count":12,"metadata":{"id":"40798A95334C4A85812FCB8A3CE6DD75","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["job 卡方检验p值: 0.0000\n","marital 卡方检验p值: 0.0000\n","education 卡方检验p值: 0.0000\n","default 卡方检验p值: 0.0001\n","housing 卡方检验p值: 0.0000\n","loan 卡方检验p值: 0.0000\n","contact 卡方检验p值: 0.0000\n","month 卡方检验p值: 0.0000\n","poutcome 卡方检验p值: 0.0000\n"]}],"source":"for col in str_features:\n    obs=pd.crosstab(train['y'],\n                    train[col],\n                    rownames=['y'],\n                    colnames=[col])\n    chi2, p, dof, expect = chi2_contingency(obs)\n    print(\"{} 卡方检验p值: {:.4f}\".format(col,p))"},{"cell_type":"markdown","metadata":{"id":"30A24D4C89AF4BDAA0115FDF8F7C12E5","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"**对连续变量做方差分析进行特征筛选**\n"},{"cell_type":"code","execution_count":13,"metadata":{"id":"43185B05C4AB48628437C4224F86F348","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["scores_: [  13.38856992   84.16396612   25.76507245 4405.56959938  193.97418155\n","  296.33099313  199.09942912]\n","pvalues_: [2.53676251e-04 4.89124305e-20 3.88332900e-07 0.00000000e+00\n"," 6.26768275e-44 4.93591331e-66 4.86613654e-45]\n","selected index: [0 1 2 3 4 5 6]\n"]}],"source":"from sklearn.feature_selection import SelectKBest,f_classif\n\nf,p=f_classif(train[num_features],train['y'])\nk = f.shape[0] - (p > 0.05).sum()\nselector = SelectKBest(f_classif, k=k)\nselector.fit(train[num_features],train['y'])\n\nprint('scores_:',selector.scores_)\nprint('pvalues_:',selector.pvalues_)\nprint('selected index:',selector.get_support(True))"},{"cell_type":"code","execution_count":14,"metadata":{"collapsed":true,"id":"5AC3B3DFF3134BDC83E167086A48540B","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"# 标准化，返回值为标准化后的数据\nstandardScaler=StandardScaler()\nss=standardScaler.fit(dummy_train.loc[:,num_features])\ndummy_train.loc[:,num_features]=ss.transform(dummy_train.loc[:,num_features])\ndummy_test.loc[:,num_features]=ss.transform(dummy_test.loc[:,num_features])"},{"cell_type":"code","execution_count":15,"metadata":{"collapsed":true,"id":"EEEA48883D2E4FA58FB392991E531C86","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"X=dummy_train\ny=train['y']"},{"cell_type":"markdown","metadata":{"id":"A16ACA2680A243C0880DE1F19CFB6AAD","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"**因为后续是进行回归而非分类，个人认为没有必要进行不平衡处理，故此部分就注释掉了**"},{"cell_type":"code","execution_count":16,"metadata":{"collapsed":true,"id":"AFE7C8F818B148178A42245BBF4C8878","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"# X_train,X_valid,y_train,y_valid=train_test_split(X,y,test_size=0.2,random_state=2020)"},{"cell_type":"code","execution_count":17,"metadata":{"collapsed":true,"id":"046EFE395ED34BFCAD11F2721BBE40FD","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":"# smote_tomek = SMOTETomek(random_state=2020)\n# X_resampled, y_resampled = smote_tomek.fit_resample(X, y)"},{"cell_type":"markdown","metadata":{"id":"13F7B7216054465D9437CEB872026BC3","jupyter":{},"mdEditEnable":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":"# 数据建模"},{"cell_type":"code","execution_count":19,"metadata":{"id":"0E1FC19C7DF74042B6F1A9F5527AA4AE","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["E:\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\utils.py:13: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"]}],"source":"#----------建模工具--------------#\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import KFold,RepeatedKFold\nimport lightgbm as lgb\nfrom sklearn.ensemble import RandomForestRegressor\nimport xgboost as xgb\nfrom xgboost import XGBRegressor\nfrom sklearn.linear_model import BayesianRidge\nfrom catboost import CatBoostRegressor, Pool\nfrom lightgbm import LGBMRegressor\n#----------模型评估工具----------#\nfrom sklearn.metrics import confusion_matrix # 混淆矩阵\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import recall_score,f1_score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import roc_curve,auc\nfrom sklearn.metrics import roc_auc_score"},{"cell_type":"markdown","metadata":{"id":"D59F8B42DDB34650B81C23D56AE0BB43","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"## 模型建立和参数调整"},{"cell_type":"markdown","metadata":{"id":"B6C0DDA8DA53482ABA9E2EE753DB105D","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于GridSearchCV的随机森林参数调整"},{"cell_type":"code","execution_count":45,"metadata":{"id":"4451159F5E62465CB52012889AD801B2","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 3 folds for each of 3 candidates, totalling 9 fits\n","{'max_features': 11, 'min_samples_leaf': 1, 'n_estimators': 1700}\n"]}],"source":"# 随机森林\n# param = {'n_estimators':[1500,1700,2000],\n#          'max_features':[7,11,15]\n#         }\n# gs = GridSearchCV(estimator=RandomForestRegressor(), param_grid=param, cv=3, scoring=\"neg_mean_squared_error\", n_jobs=-1, verbose=10) \n# gs.fit(X_resampled,y_resampled)\n# print(gs.best_params_) \n"},{"cell_type":"markdown","metadata":{"id":"821CD114AA87471B9188366261086B72","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于五折交叉验证的随机森林"},{"cell_type":"code","execution_count":46,"metadata":{"id":"46ED98A1821C450E85F2338B49A197C6","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["0.929373220326099\n"]}],"source":"n_fold = 5\nfolds = KFold(n_splits=n_fold, shuffle=True, random_state=2022)\noof_rf = np.zeros(len(X))\nprediction_rf = np.zeros(len(dummy_test))\nfor fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n    y_train, y_valid = y[train_index], y[valid_index]\n#     smote_tomek = SMOTETomek(random_state=2022)\n#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n    model_rf = RandomForestRegressor(max_features=11,min_samples_leaf=1,n_estimators=1700,random_state=2022).fit(X_train,y_train)\n    y_pred_valid = model_rf.predict(X_valid)\n    y_pred = model_rf.predict(dummy_test)\n    oof_rf[valid_index] = y_pred_valid.reshape(-1, )\n    prediction_rf += y_pred\nprediction_rf /= n_fold \nprint(roc_auc_score(y, oof_rf))\n#0.929373220326099"},{"cell_type":"markdown","metadata":{"id":"A03808ADDE7A4AC4AEFDC87F09A5A017","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于GridSearchCV的XGB参数调整"},{"cell_type":"code","execution_count":123,"metadata":{"id":"A26971BEEF684860B739522723029593","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 3 folds for each of 3 candidates, totalling 9 fits\n","{'colsample_bytree': 0.6, 'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 800, 'subsample': 0.8}\n"]}],"source":"# param = {'max_depth': [3],\n#          'learning_rate': [0.01],\n#         'subsample':[0.8],\n#         'colsample_bytree':[0.6],\n#          'n_estimators': [8000]\n\n#         }\n# gs = GridSearchCV(estimator=XGBRegressor(), param_grid=param, cv=3, scoring=\"neg_mean_squared_error\", n_jobs=-1, verbose=10) \n# gs.fit(X,y)\n# print(gs.best_params_) \n"},{"cell_type":"markdown","metadata":{"id":"7AF59354E891478D8154F963C5EDE251","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于五折交叉验证的XGB"},{"cell_type":"code","execution_count":126,"metadata":{"id":"C0C60825A129420F8F4C983DEC926AC6","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["[0]\tvalidation_0-auc:0.86541\n","[1]\tvalidation_0-auc:0.88478\n","[2]\tvalidation_0-auc:0.89516\n","[3]\tvalidation_0-auc:0.90166\n","[4]\tvalidation_0-auc:0.90600\n","[5]\tvalidation_0-auc:0.90680\n","[6]\tvalidation_0-auc:0.90747\n","[7]\tvalidation_0-auc:0.90969\n","[8]\tvalidation_0-auc:0.90979\n","[9]\tvalidation_0-auc:0.90992\n","[10]\tvalidation_0-auc:0.91239\n"]},{"name":"stderr","output_type":"stream","text":["E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n","  UserWarning,\n","E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n","  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UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n","  UserWarning,\n","E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n","  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UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n","  UserWarning,\n","E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n","  UserWarning,\n"]},{"name":"stdout","output_type":"stream","text":"[12]\tvalidation_0-auc:0.92002\n[13]\tvalidation_0-auc:0.92045\n[14]\tvalidation_0-auc:0.92028\n[15]\tvalidation_0-auc:0.92117\n[16]\tvalidation_0-auc:0.92060\n[17]\tvalidation_0-auc:0.92007\n[18]\tvalidation_0-auc:0.92012\n[19]\tvalidation_0-auc:0.92056\n[20]\tvalidation_0-auc:0.92115\n[21]\tvalidation_0-auc:0.92272\n[22]\tvalidation_0-auc:0.92302\n[23]\tvalidation_0-auc:0.92345\n[24]\tvalidation_0-auc:0.92380\n[25]\tvalidation_0-auc:0.92478\n[26]\tvalidation_0-auc:0.92474\n[27]\tvalidation_0-auc:0.92540\n[28]\tvalidation_0-auc:0.92551\n[29]\tvalidation_0-auc:0.92551\n[30]\tvalidation_0-auc:0.92604\n[31]\tvalidation_0-auc:0.92591\n[32]\tvalidation_0-auc:0.92639\n[33]\tvalidation_0-auc:0.92736\n[34]\tvalidation_0-auc:0.92697\n[35]\tvalidation_0-auc:0.92699\n[36]\tvalidation_0-auc:0.92696\n[37]\tvalidation_0-auc:0.92697\n[38]\tvalidation_0-auc:0.92672\n[39]\tvalidation_0-auc:0.92667\n[40]\tvalidation_0-auc:0.92712\n[41]\tvalidation_0-auc:0.92706\n[42]\tvalidation_0-auc:0.92704\n[43]\tvalidation_0-auc:0.92712\n[44]\tvalidation_0-auc:0.92666\n[45]\tvalidation_0-auc:0.92658\n[46]\tvalidation_0-auc:0.92653\n[47]\tvalidation_0-auc:0.92649\n[48]\tvalidation_0-auc:0.92651\n[49]\tvalidation_0-auc:0.92647\n[50]\tvalidation_0-auc:0.92653\n[51]\tvalidation_0-auc:0.92679\n[52]\tvalidation_0-auc:0.92676\n[53]\tvalidation_0-auc:0.92684\n[54]\tvalidation_0-auc:0.92712\n[55]\tvalidation_0-auc:0.92728\n[56]\tvalidation_0-auc:0.92727\n[57]\tvalidation_0-auc:0.92732\n[58]\tvalidation_0-auc:0.92721\n[59]\tvalidation_0-auc:0.92728\n[60]\tvalidation_0-auc:0.92753\n[61]\tvalidation_0-auc:0.92751\n[62]\tvalidation_0-auc:0.92750\n[63]\tvalidation_0-auc:0.92753\n[64]\tvalidation_0-auc:0.92756\n[65]\tvalidation_0-auc:0.92754\n[66]\tvalidation_0-auc:0.92760\n[67]\tvalidation_0-auc:0.92749\n[68]\tvalidation_0-auc:0.92738\n[69]\tvalidation_0-auc:0.92746\n[70]\tvalidation_0-auc:0.92767\n[71]\tvalidation_0-auc:0.92795\n[72]\tvalidation_0-auc:0.92798\n[73]\tvalidation_0-auc:0.92780\n[74]\tvalidation_0-auc:0.92772\n[75]\tvalidation_0-auc:0.92786\n[76]\tvalidation_0-auc:0.92789\n[77]\tvalidation_0-auc:0.92800\n[78]\tvalidation_0-auc:0.92822\n[79]\tvalidation_0-auc:0.92836\n[80]\tvalidation_0-auc:0.92846\n[81]\tvalidation_0-auc:0.92868\n[82]\tvalidation_0-auc:0.92875\n[83]\tvalidation_0-auc:0.92887\n[84]\tvalidation_0-auc:0.92901\n[85]\tvalidation_0-auc:0.92906\n[86]\tvalidation_0-auc:0.92900\n[87]\tvalidation_0-auc:0.92919\n[88]\tvalidation_0-auc:0.92927\n[89]\tvalidation_0-auc:0.92970\n[90]\tvalidation_0-auc:0.92981\n[91]\tvalidation_0-auc:0.92983\n[92]\tvalidation_0-auc:0.92990\n[93]\tvalidation_0-auc:0.93004\n[94]\tvalidation_0-auc:0.93023\n[95]\tvalidation_0-auc:0.93021\n[96]\tvalidation_0-auc:0.93024\n[97]\tvalidation_0-auc:0.93021\n[98]\tvalidation_0-auc:0.93031\n[99]\tvalidation_0-auc:0.93070\n[100]\tvalidation_0-auc:0.93070\n[101]\tvalidation_0-auc:0.93074\n[102]\tvalidation_0-auc:0.93070\n[103]\tvalidation_0-auc:0.93078\n[104]\tvalidation_0-auc:0.93084\n[105]\tvalidation_0-auc:0.93085\n[106]\tvalidation_0-auc:0.93093\n[107]\tvalidation_0-auc:0.93099\n[108]\tvalidation_0-auc:0.93100\n[109]\tvalidation_0-auc:0.93098\n[110]\tvalidation_0-auc:0.93103\n[111]\tvalidation_0-auc:0.93102\n[112]\tvalidation_0-auc:0.93098\n[113]\tvalidation_0-auc:0.93099\n[114]\tvalidation_0-auc:0.93110\n[115]\tvalidation_0-auc:0.93120\n[116]\tvalidation_0-auc:0.93124\n[117]\tvalidation_0-auc:0.93125\n[118]\tvalidation_0-auc:0.93125\n[119]\tvalidation_0-auc:0.93126\n[120]\tvalidation_0-auc:0.93118\n[121]\tvalidation_0-auc:0.93100\n[122]\tvalidation_0-auc:0.93099\n[123]\tvalidation_0-auc:0.93095\n[124]\tvalidation_0-auc:0.93089\n[125]\tvalidation_0-auc:0.93086\n[126]\tvalidation_0-auc:0.93080\n[127]\tvalidation_0-auc:0.93080\n[128]\tvalidation_0-auc:0.93080\n[129]\tvalidation_0-auc:0.93093\n[130]\tvalidation_0-auc:0.93098\n[131]\tvalidation_0-auc:0.93100\n[132]\tvalidation_0-auc:0.93099\n[133]\tvalidation_0-auc:0.93100\n[134]\tvalidation_0-auc:0.93098\n[135]\tvalidation_0-auc:0.93103\n[136]\tvalidation_0-auc:0.93130\n[137]\tvalidation_0-auc:0.93132\n[138]\tvalidation_0-auc:0.93144\n[139]\tvalidation_0-auc:0.93147\n[140]\tvalidation_0-auc:0.93146\n[141]\tvalidation_0-auc:0.93156\n[142]\tvalidation_0-auc:0.93160\n[143]\tvalidation_0-auc:0.93161\n[144]\tvalidation_0-auc:0.93164\n[145]\tvalidation_0-auc:0.93167\n[146]\tvalidation_0-auc:0.93171\n[147]\tvalidation_0-auc:0.93173\n[148]\tvalidation_0-auc:0.93172\n[149]\tvalidation_0-auc:0.93169\n[150]\tvalidation_0-auc:0.93164\n[151]\tvalidation_0-auc:0.93171\n[152]\tvalidation_0-auc:0.93168\n[153]\tvalidation_0-auc:0.93167\n[154]\tvalidation_0-auc:0.93169\n[155]\tvalidation_0-auc:0.93170\n[156]\tvalidation_0-auc:0.93171\n[157]\tvalidation_0-auc:0.93173\n[158]\tvalidation_0-auc:0.93180\n[159]\tvalidation_0-auc:0.93190\n[160]\tvalidation_0-auc:0.93205\n[161]\tvalidation_0-auc:0.93202\n[162]\tvalidation_0-auc:0.93211\n[163]\tvalidation_0-auc:0.93213\n[164]\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UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n","  UserWarning,\n","E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n","  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UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n","  UserWarning,\n","E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n","  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= 5\nfolds = KFold(n_splits=n_fold, shuffle=True, random_state=2022)\noof_xgb = np.zeros(len(X))\nprediction_xgb = np.zeros(len(dummy_test))\nfor fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n    y_train, y_valid = y[train_index], y[valid_index]\n#     smote_tomek = SMOTETomek(random_state=2022)\n#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n    eval_set = [(X_valid, y_valid)]\n    model_xgb = XGBRegressor(\n        max_depth=9,learning_rate=0.01,n_estimators=10000,colsample_bytree=0.6,subsample=0.8,random_state=2022\n    ).fit(X_train,y_train,early_stopping_rounds=100, eval_metric=\"auc\",eval_set=eval_set, verbose=True)\n    y_pred_valid = model_xgb.predict(X_valid)\n    y_pred = model_xgb.predict(dummy_test)\n    oof_xgb[valid_index] = y_pred_valid.reshape(-1, )\n    prediction_xgb += y_pred\nprediction_xgb /= n_fold \nprint(roc_auc_score(y, oof_xgb))\n# 0.9326219985474677"},{"cell_type":"markdown","metadata":{"id":"D03CB6619D8B44B08E990ED3EA01C61D","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于GridSearchCV的LGBM参数调整"},{"cell_type":"code","execution_count":65,"metadata":{"id":"082D0D1C79D14C0BB07870203B74F5F8","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["{'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 30, 'n_estimators': 10000, 'num_leaves': 59, 'subsample': 0.7}\n"]}],"source":"# param = {'max_depth': [30],\n#          'learning_rate': [0.01],\n#          'num_leaves': [59],\n#          'subsample': [0.7],\n#          'colsample_bytree': [0.8],\n#          'n_estimators': [10000]}\n# gs = GridSearchCV(estimator=LGBMRegressor(), param_grid=param, cv=5, scoring=\"neg_mean_squared_error\", n_jobs=-1) \n# gs.fit(X_resampled,y_resampled)\n# print(gs.best_params_) \n"},{"cell_type":"markdown","metadata":{"id":"23ADF0543B77452A8DCC799C0582D474","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于五折交叉验证的LGBM"},{"cell_type":"code","execution_count":115,"metadata":{"id":"D2313DA33FCA4D7EA5F3C32DD98D6BBA","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n","E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"]},{"name":"stdout","output_type":"stream","text":["[50]\ttraining's auc: 0.94085\ttraining's l2: 0.0788925\tvalid_1's auc: 0.925864\tvalid_1's l2: 0.0818997\n","[100]\ttraining's auc: 0.944802\ttraining's l2: 0.0672695\tvalid_1's auc: 0.928742\tvalid_1's l2: 0.0719714\n","[150]\ttraining's auc: 0.948559\ttraining's l2: 0.0607984\tvalid_1's auc: 0.930343\tvalid_1's l2: 0.0672457\n","[200]\ttraining's auc: 0.951549\ttraining's l2: 0.056876\tvalid_1's auc: 0.931374\tvalid_1's l2: 0.0649636\n","[250]\ttraining's auc: 0.954385\ttraining's l2: 0.054275\tvalid_1's auc: 0.931987\tvalid_1's l2: 0.0639735\n","[300]\ttraining's auc: 0.957015\ttraining's l2: 0.0521878\tvalid_1's auc: 0.932396\tvalid_1's l2: 0.0634468\n","[350]\ttraining's auc: 0.960145\ttraining's l2: 0.0503487\tvalid_1's auc: 0.93328\tvalid_1's l2: 0.0630066\n","[400]\ttraining's auc: 0.962937\ttraining's l2: 0.0486323\tvalid_1's auc: 0.93371\tvalid_1's l2: 0.062822\n","[450]\ttraining's auc: 0.965233\ttraining's l2: 0.0471381\tvalid_1's auc: 0.933886\tvalid_1's l2: 0.0627358\n","[500]\ttraining's auc: 0.96724\ttraining's l2: 0.0458021\tvalid_1's auc: 0.934201\tvalid_1's l2: 0.0626506\n","[550]\ttraining's auc: 0.969189\ttraining's l2: 0.0445553\tvalid_1's auc: 0.934529\tvalid_1's l2: 0.0624507\n","[600]\ttraining's auc: 0.970941\ttraining's l2: 0.0434685\tvalid_1's auc: 0.93459\tvalid_1's l2: 0.0623492\n","[650]\ttraining's auc: 0.972602\ttraining's l2: 0.0424998\tvalid_1's auc: 0.934349\tvalid_1's l2: 0.0622475\n","[700]\ttraining's auc: 0.974021\ttraining's l2: 0.041611\tvalid_1's auc: 0.934276\tvalid_1's l2: 0.0621712\n","[750]\ttraining's auc: 0.975347\ttraining's l2: 0.0408109\tvalid_1's auc: 0.933644\tvalid_1's l2: 0.0622134\n","[800]\ttraining's auc: 0.976582\ttraining's l2: 0.0400338\tvalid_1's auc: 0.933249\tvalid_1's l2: 0.0622394\n"]},{"name":"stderr","output_type":"stream","text":["E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n","E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"]},{"name":"stdout","output_type":"stream","text":["[50]\ttraining's auc: 0.9402\ttraining's l2: 0.07935\tvalid_1's auc: 0.932536\tvalid_1's l2: 0.0801626\n","[100]\ttraining's auc: 0.943739\ttraining's l2: 0.0676194\tvalid_1's auc: 0.934496\tvalid_1's l2: 0.0702667\n","[150]\ttraining's auc: 0.947848\ttraining's l2: 0.061121\tvalid_1's auc: 0.936416\tvalid_1's l2: 0.065416\n","[200]\ttraining's auc: 0.950776\ttraining's l2: 0.0572456\tvalid_1's auc: 0.937341\tvalid_1's l2: 0.0630831\n","[250]\ttraining's auc: 0.953983\ttraining's l2: 0.0545583\tvalid_1's auc: 0.938133\tvalid_1's l2: 0.0619445\n","[300]\ttraining's auc: 0.956913\ttraining's l2: 0.0523396\tvalid_1's auc: 0.93872\tvalid_1's l2: 0.0612938\n","[350]\ttraining's auc: 0.959894\ttraining's l2: 0.0504202\tvalid_1's auc: 0.939454\tvalid_1's l2: 0.0609051\n","[400]\ttraining's auc: 0.962519\ttraining's l2: 0.0487443\tvalid_1's auc: 0.939785\tvalid_1's l2: 0.0607129\n","[450]\ttraining's auc: 0.964735\ttraining's l2: 0.0473303\tvalid_1's auc: 0.939786\tvalid_1's l2: 0.0606189\n","[500]\ttraining's auc: 0.966875\ttraining's l2: 0.0459614\tvalid_1's auc: 0.940058\tvalid_1's l2: 0.0605379\n","[550]\ttraining's auc: 0.968779\ttraining's l2: 0.0447104\tvalid_1's auc: 0.94022\tvalid_1's l2: 0.0605152\n","[600]\ttraining's auc: 0.97058\ttraining's l2: 0.0435918\tvalid_1's auc: 0.940533\tvalid_1's l2: 0.0604589\n","[650]\ttraining's auc: 0.972085\ttraining's l2: 0.0426417\tvalid_1's auc: 0.940571\tvalid_1's l2: 0.0604517\n","[700]\ttraining's auc: 0.973432\ttraining's l2: 0.0417863\tvalid_1's auc: 0.940672\tvalid_1's l2: 0.0604522\n","[750]\ttraining's auc: 0.974732\ttraining's l2: 0.0409048\tvalid_1's auc: 0.940905\tvalid_1's l2: 0.0603683\n","[800]\ttraining's auc: 0.975899\ttraining's l2: 0.0401361\tvalid_1's auc: 0.940962\tvalid_1's l2: 0.0603816\n","[850]\ttraining's auc: 0.976931\ttraining's l2: 0.0394491\tvalid_1's auc: 0.940966\tvalid_1's l2: 0.0603915\n","[900]\ttraining's auc: 0.977929\ttraining's l2: 0.0387792\tvalid_1's auc: 0.94085\tvalid_1's l2: 0.0604162\n","[950]\ttraining's auc: 0.978862\ttraining's l2: 0.0381312\tvalid_1's auc: 0.940734\tvalid_1's l2: 0.0604662\n"]},{"name":"stderr","output_type":"stream","text":["E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n","E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"]},{"name":"stdout","output_type":"stream","text":["[50]\ttraining's auc: 0.941654\ttraining's l2: 0.0795005\tvalid_1's auc: 0.922594\tvalid_1's l2: 0.0787988\n","[100]\ttraining's auc: 0.945571\ttraining's l2: 0.0677357\tvalid_1's auc: 0.92527\tvalid_1's l2: 0.0696453\n","[150]\ttraining's auc: 0.949356\ttraining's l2: 0.0611459\tvalid_1's auc: 0.926724\tvalid_1's l2: 0.0655987\n","[200]\ttraining's auc: 0.952608\ttraining's l2: 0.0570597\tvalid_1's auc: 0.928057\tvalid_1's l2: 0.0637265\n","[250]\ttraining's auc: 0.955616\ttraining's l2: 0.0543341\tvalid_1's auc: 0.928873\tvalid_1's l2: 0.0630037\n","[300]\ttraining's auc: 0.95832\ttraining's l2: 0.0522183\tvalid_1's auc: 0.929336\tvalid_1's l2: 0.0626532\n","[350]\ttraining's auc: 0.961151\ttraining's l2: 0.0503588\tvalid_1's auc: 0.930429\tvalid_1's l2: 0.0623272\n","[400]\ttraining's auc: 0.963634\ttraining's l2: 0.0487219\tvalid_1's auc: 0.93069\tvalid_1's l2: 0.0622242\n","[450]\ttraining's auc: 0.965829\ttraining's l2: 0.0472595\tvalid_1's auc: 0.930543\tvalid_1's l2: 0.0621943\n","[500]\ttraining's auc: 0.967929\ttraining's l2: 0.0459291\tvalid_1's auc: 0.930614\tvalid_1's l2: 0.0621045\n","[550]\ttraining's auc: 0.969991\ttraining's l2: 0.0446926\tvalid_1's auc: 0.930791\tvalid_1's l2: 0.0620086\n","[600]\ttraining's auc: 0.971611\ttraining's l2: 0.0436567\tvalid_1's auc: 0.930556\tvalid_1's l2: 0.0619909\n","[650]\ttraining's auc: 0.973089\ttraining's l2: 0.0427368\tvalid_1's auc: 0.930451\tvalid_1's l2: 0.0619711\n","[700]\ttraining's auc: 0.974456\ttraining's l2: 0.0418338\tvalid_1's auc: 0.930732\tvalid_1's l2: 0.0618934\n","[750]\ttraining's auc: 0.975701\ttraining's l2: 0.0409687\tvalid_1's auc: 0.93121\tvalid_1's l2: 0.0617997\n","[800]\ttraining's auc: 0.976774\ttraining's l2: 0.04021\tvalid_1's auc: 0.931229\tvalid_1's l2: 0.0618136\n","[850]\ttraining's auc: 0.977858\ttraining's l2: 0.0394732\tvalid_1's auc: 0.931142\tvalid_1's l2: 0.061852\n","[900]\ttraining's auc: 0.978878\ttraining's l2: 0.0387557\tvalid_1's auc: 0.930934\tvalid_1's l2: 0.0618529\n","[950]\ttraining's auc: 0.979878\ttraining's l2: 0.0380331\tvalid_1's auc: 0.930689\tvalid_1's l2: 0.0618732\n"]},{"name":"stderr","output_type":"stream","text":["E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n","E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"]},{"name":"stdout","output_type":"stream","text":["[50]\ttraining's auc: 0.938779\ttraining's l2: 0.0783692\tvalid_1's auc: 0.929724\tvalid_1's l2: 0.0849279\n","[100]\ttraining's auc: 0.943638\ttraining's l2: 0.0667337\tvalid_1's auc: 0.932269\tvalid_1's l2: 0.074493\n","[150]\ttraining's auc: 0.94752\ttraining's l2: 0.0603255\tvalid_1's auc: 0.933807\tvalid_1's l2: 0.0694419\n","[200]\ttraining's auc: 0.950881\ttraining's l2: 0.056408\tvalid_1's auc: 0.93552\tvalid_1's l2: 0.0669414\n","[250]\ttraining's auc: 0.95406\ttraining's l2: 0.0536874\tvalid_1's auc: 0.935899\tvalid_1's l2: 0.0658231\n","[300]\ttraining's auc: 0.957231\ttraining's l2: 0.05144\tvalid_1's auc: 0.936191\tvalid_1's l2: 0.0651672\n","[350]\ttraining's auc: 0.960257\ttraining's l2: 0.0495814\tvalid_1's auc: 0.936883\tvalid_1's l2: 0.064655\n","[400]\ttraining's auc: 0.962809\ttraining's l2: 0.0479463\tvalid_1's auc: 0.937101\tvalid_1's l2: 0.0644035\n","[450]\ttraining's auc: 0.965109\ttraining's l2: 0.0464814\tvalid_1's auc: 0.937094\tvalid_1's l2: 0.0642898\n","[500]\ttraining's auc: 0.967202\ttraining's l2: 0.0451684\tvalid_1's auc: 0.937229\tvalid_1's l2: 0.0641968\n","[550]\ttraining's auc: 0.969275\ttraining's l2: 0.0439568\tvalid_1's auc: 0.937514\tvalid_1's l2: 0.0640906\n","[600]\ttraining's auc: 0.971103\ttraining's l2: 0.0428949\tvalid_1's auc: 0.937774\tvalid_1's l2: 0.0639556\n","[650]\ttraining's auc: 0.972795\ttraining's l2: 0.0419193\tvalid_1's auc: 0.938085\tvalid_1's l2: 0.0638854\n","[700]\ttraining's auc: 0.974376\ttraining's l2: 0.0409977\tvalid_1's auc: 0.938299\tvalid_1's l2: 0.0637705\n","[750]\ttraining's auc: 0.975719\ttraining's l2: 0.0401733\tvalid_1's auc: 0.938254\tvalid_1's l2: 0.0638389\n","[800]\ttraining's auc: 0.976952\ttraining's l2: 0.039378\tvalid_1's auc: 0.938171\tvalid_1's l2: 0.0638989\n","[850]\ttraining's auc: 0.977996\ttraining's l2: 0.0386485\tvalid_1's auc: 0.93817\tvalid_1's l2: 0.0639324\n","[900]\ttraining's auc: 0.978923\ttraining's l2: 0.0380388\tvalid_1's auc: 0.938106\tvalid_1's l2: 0.0639588\n"]},{"name":"stderr","output_type":"stream","text":["E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n","E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n","  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"]},{"name":"stdout","output_type":"stream","text":["[50]\ttraining's auc: 0.941106\ttraining's l2: 0.0789061\tvalid_1's auc: 0.916192\tvalid_1's l2: 0.0814378\n","[100]\ttraining's auc: 0.945514\ttraining's l2: 0.0669373\tvalid_1's auc: 0.919579\tvalid_1's l2: 0.0723827\n","[150]\ttraining's auc: 0.949487\ttraining's l2: 0.0604246\tvalid_1's auc: 0.921699\tvalid_1's l2: 0.068301\n","[200]\ttraining's auc: 0.953036\ttraining's l2: 0.0563975\tvalid_1's auc: 0.922555\tvalid_1's l2: 0.0664568\n","[250]\ttraining's auc: 0.955869\ttraining's l2: 0.0536905\tvalid_1's auc: 0.923021\tvalid_1's l2: 0.0656804\n","[300]\ttraining's auc: 0.958501\ttraining's l2: 0.0515648\tvalid_1's auc: 0.923196\tvalid_1's l2: 0.0654114\n","[350]\ttraining's auc: 0.961319\ttraining's l2: 0.0497304\tvalid_1's auc: 0.924204\tvalid_1's l2: 0.0651377\n","[400]\ttraining's auc: 0.963981\ttraining's l2: 0.0480409\tvalid_1's auc: 0.924832\tvalid_1's l2: 0.0648762\n","[450]\ttraining's auc: 0.96626\ttraining's l2: 0.0466098\tvalid_1's auc: 0.925281\tvalid_1's l2: 0.0647658\n","[500]\ttraining's auc: 0.968523\ttraining's l2: 0.0452797\tvalid_1's auc: 0.925673\tvalid_1's l2: 0.0647242\n","[550]\ttraining's auc: 0.970423\ttraining's l2: 0.0440425\tvalid_1's auc: 0.926443\tvalid_1's l2: 0.0646256\n","[600]\ttraining's auc: 0.972048\ttraining's l2: 0.0429566\tvalid_1's auc: 0.926666\tvalid_1's l2: 0.064639\n","[650]\ttraining's auc: 0.97356\ttraining's l2: 0.0419683\tvalid_1's auc: 0.926761\tvalid_1's l2: 0.0646741\n","[700]\ttraining's auc: 0.974844\ttraining's l2: 0.0411034\tvalid_1's auc: 0.926648\tvalid_1's l2: 0.0647594\n","[750]\ttraining's auc: 0.976116\ttraining's l2: 0.0402927\tvalid_1's auc: 0.92654\tvalid_1's l2: 0.0648155\n","0.9342991211145983\n"]}],"source":"n_fold = 5\nfolds = KFold(n_splits=n_fold, shuffle=True,random_state=1314)\nparams = {\n    'learning_rate':0.01,\n    'subsample': 0.7,\n    'num_leaves': 59,\n    'n_estimators':1500,\n    'max_depth': 30,\n    'colsample_bytree': 0.8,\n    'verbose': -1,\n    'seed': 2022,\n    'n_jobs': -1\n}\n\noof_lgb = np.zeros(len(X))\npredictions_lgb  = np.zeros(len(dummy_test))\nfor fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n    y_train, y_valid = y[train_index], y[valid_index]\n#     smote_tomek = SMOTETomek(random_state=2022)\n#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n    model = lgb.LGBMRegressor(**params)\n    model.fit(X_train, y_train,\n              eval_set=[(X_train, y_train), (X_valid, y_valid)],\n              eval_metric='auc',\n              verbose=50, early_stopping_rounds=200)\n    y_pred_valid = model.predict(X_valid)\n    y_pred = model.predict(dummy_test, num_iteration=model.best_iteration_)\n    oof_lgb[valid_index] = y_pred_valid.reshape(-1, )\n    predictions_lgb  += y_pred\npredictions_lgb  /= n_fold\nprint(roc_auc_score(y, oof_lgb))\n# 0.9342991211145983"},{"cell_type":"markdown","metadata":{"id":"1D0CF99ED0F04505ADAF99D733B04257","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于GridSearchCV的catboost参数调整"},{"cell_type":"code","execution_count":1,"metadata":{"collapsed":true,"id":"40143CE0A1F7461A9D5AAFEB614609B5","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[],"source":"\n# param = {'depth': [7,9,11],\n#          'learning_rate': [0.01],\n#          'iterations':  [8000]}\n# gs = GridSearchCV(estimator=CatBoostRegressor(), param_grid=param, cv=3, scoring=\"neg_mean_squared_error\", n_jobs=-1) \n# gs.fit(X_resampled,y_resampled)\n# print(gs.best_params_) "},{"cell_type":"markdown","metadata":{"id":"0E595CF0D95341E69AB1154B1849F14E","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于五折交叉验证的catboost"},{"cell_type":"code","execution_count":67,"metadata":{"id":"3A557EC583C0481488702DC367D10F53","jupyter":{},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["0:\tlearn: 0.3201355\ttest: 0.3215319\tbest: 0.3215319 (0)\ttotal: 55.1ms\tremaining: 22m 57s\n","1000:\tlearn: 0.2210492\ttest: 0.2482059\tbest: 0.2482059 (1000)\ttotal: 34.7s\tremaining: 13m 51s\n","Stopped by overfitting detector  (300 iterations wait)\n","\n","bestTest = 0.2468782072\n","bestIteration = 1477\n","\n","Shrink model to first 1478 iterations.\n","0:\tlearn: 0.3208935\ttest: 0.3185105\tbest: 0.3185105 (0)\ttotal: 69.5ms\tremaining: 28m 57s\n","1000:\tlearn: 0.2206024\ttest: 0.2464808\tbest: 0.2464751 (998)\ttotal: 37.8s\tremaining: 15m 5s\n","2000:\tlearn: 0.1971871\ttest: 0.2454924\tbest: 0.2454188 (1725)\ttotal: 1m 13s\tremaining: 14m 5s\n","Stopped by overfitting detector  (300 iterations wait)\n","\n","bestTest = 0.2454188002\n","bestIteration = 1725\n","\n","Shrink model to first 1726 iterations.\n","0:\tlearn: 0.3214860\ttest: 0.3161403\tbest: 0.3161403 (0)\ttotal: 42ms\tremaining: 17m 30s\n","1000:\tlearn: 0.2210140\ttest: 0.2480678\tbest: 0.2480521 (995)\ttotal: 36.2s\tremaining: 14m 27s\n","Stopped by overfitting detector  (300 iterations wait)\n","\n","bestTest = 0.24699407\n","bestIteration = 1556\n","\n","Shrink model to first 1557 iterations.\n","0:\tlearn: 0.3186885\ttest: 0.3274174\tbest: 0.3274174 (0)\ttotal: 54ms\tremaining: 22m 30s\n","1000:\tlearn: 0.2202664\ttest: 0.2525361\tbest: 0.2525361 (1000)\ttotal: 36.4s\tremaining: 14m 32s\n","Stopped by overfitting detector  (300 iterations wait)\n","\n","bestTest = 0.2515410448\n","bestIteration = 1603\n","\n","Shrink model to first 1604 iterations.\n","0:\tlearn: 0.3209030\ttest: 0.3185929\tbest: 0.3185929 (0)\ttotal: 75.6ms\tremaining: 31m 29s\n","1000:\tlearn: 0.2212537\ttest: 0.2523067\tbest: 0.2523067 (1000)\ttotal: 34.7s\tremaining: 13m 51s\n","Stopped by overfitting detector  (300 iterations wait)\n","\n","bestTest = 0.250998901\n","bestIteration = 1618\n","\n","Shrink model to first 1619 iterations.\n"]}],"source":"# 本地交叉验证\nn_fold = 5\nfolds = KFold(n_splits=n_fold, shuffle=True, random_state=1314)\n\noof_cat = np.zeros(len(X))\nprediction_cat = np.zeros(len(dummy_test))\nfor fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n    y_train, y_valid = y[train_index], y[valid_index]\n#     smote_tomek = SMOTETomek(random_state=2022)\n#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n    train_pool = Pool(X_train, y_train)\n    eval_pool = Pool(X_valid, y_valid)\n    cbt_model = CatBoostRegressor(iterations=25000, # 注：baseline 提到的分数是用 iterations=60000 得到的，但运行时间有点久\n                           learning_rate=0.01, # 注：事实上好几个 property 在 lr=0.1 时收敛巨慢。后面可以考虑调大\n#                            eval_metric='SMAPE',\n                                  depth=9,\n                           use_best_model=True,\n                           random_seed=2022,\n                           logging_level='Verbose',\n                           #task_type='GPU',\n                           devices='0',\n                           gpu_ram_part=0.5,\n                           early_stopping_rounds=300)\n    \n    cbt_model.fit(train_pool,\n              eval_set=eval_pool,\n              verbose=1000)\n\n    y_pred_valid = cbt_model.predict(X_valid)\n    y_pred_c = cbt_model.predict(dummy_test)\n    oof_cat[valid_index] = y_pred_valid.reshape(-1, )\n    prediction_cat += y_pred_c\nprediction_cat /= n_fold \n"},{"cell_type":"code","execution_count":68,"metadata":{"id":"27A63B61DB094FCF95CEEB6A1E3ADC5A","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["0.935264298588153\n"]}],"source":"print(roc_auc_score(y, oof_cat))\n# 0.935264298588153"},{"cell_type":"markdown","metadata":{"id":"9470A77D276E418F9CB2ACD8D18E6F68","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"### 基于stacking的模型融合"},{"cell_type":"code","execution_count":71,"metadata":{"id":"4A24F209D61E47E7A27F4A26D6687B89","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["fold 0\n","fold 1\n","fold 2\n","fold 3\n","fold 4\n","fold 5\n","fold 6\n","fold 7\n","fold 8\n","fold 9\n","CV score: 0.06156606\n"]}],"source":"# from sklearn.linear_model import Bayesian\nfrom sklearn.metrics import mean_squared_error,mean_absolute_error,make_scorer\n\n# 将多个模型的结果进行stacking（叠加）\ntrain_stack = np.vstack([oof_rf,oof_lgb,oof_cat,oof_xgb]).transpose()\ntest_stack = np.vstack([prediction_rf,prediction_lgb,prediction_cat,prediction_xgb]).transpose()\n#贝叶斯分类器也使用交叉验证的方法，5折，重复2次\nfolds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=2018)\noof_stack = np.zeros(train_stack.shape[0])\npredictions = np.zeros(test_stack.shape[0])\n \nfor fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack,y)):\n    print(\"fold {}\".format(fold_))\n    trn_data, trn_y = train_stack[trn_idx], y.iloc[trn_idx].values\n    val_data, val_y = train_stack[val_idx], y.iloc[val_idx].values#\n    \n    clf_3 = BayesianRidge()\n    clf_3.fit(trn_data, trn_y)\n    \n    oof_stack[val_idx] = clf_3.predict(val_data)#对验证集有一个预测，用于后面计算模型的偏差\n    predictions += clf_3.predict(test_stack) / 10#对测试集的预测，除以10是因为5折交叉验证重复了2次\n    \nmean_squared_error(y.values, oof_stack)#计算出模型在训练集上的均方误差\nprint(\"CV score: {:<8.8f}\".format(mean_squared_error(y.values, oof_stack)))"},{"cell_type":"code","execution_count":72,"metadata":{"id":"33AB2F850236462DB7F0DFE549A2C559","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["0.9361018703876826\n"]}],"source":"print(roc_auc_score(y, oof_stack))\n# 0.9361018703876826"},{"cell_type":"markdown","metadata":{"id":"1D7C809913E14DD79D1E399F519EDC1C","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"source":"# 保存结果"},{"cell_type":"code","execution_count":73,"metadata":{"collapsed":true,"id":"BC37DA49289D45C2951D2FFDC91043BA","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"outputs":[],"source":"test['pred'] = predictions\ntest[['ID', 'pred']].to_csv(r'C:\\Users\\hepei\\Desktop\\比赛代码\\练习赛\\客户购买预测\\结果\\sub.csv', index=None, encoding=\"utf-8\")"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python","nbconvert_exporter":"python","file_extension":".py","version":"3.5.2","pygments_lexer":"ipython3"}},"nbformat":4,"nbformat_minor":2}